Display options
Cite
Saeedghalati M, Abbassian A. Modeling spatio-temporal dynamics of network damage and network recovery. Front Comput Neurosci. 2015;9:130doi: 10.3389/fncom.2015.00130.
Saeedghalati, M., & Abbassian, A. (2015). Modeling spatio-temporal dynamics of network damage and network recovery. Frontiers in computational neuroscience, 9130. https://doi.org/10.3389/fncom.2015.00130
Saeedghalati, Mohammadkarim, and Abbassian, Abdolhosein. "Modeling spatio-temporal dynamics of network damage and network recovery." Frontiers in computational neuroscience vol. 9 (2015): 130. doi: https://doi.org/10.3389/fncom.2015.00130
Saeedghalati M, Abbassian A. Modeling spatio-temporal dynamics of network damage and network recovery. Front Comput Neurosci. 2015 Oct 22;9:130. doi: 10.3389/fncom.2015.00130. eCollection 2015. PMID: 26557071; PMCID: PMC4614320.
Copy Download .nbib Format: NLM
Share it on

Front Comput Neurosci. 2015 Oct 22;9:130. doi: 10.3389/fncom.2015.00130. eCollection 2015.

Modeling spatio-temporal dynamics of network damage and network recovery.

Frontiers in computational neuroscience

Mohammadkarim Saeedghalati, Abdolhosein Abbassian

Affiliations

  1. BioMath, School of Mathematics, Institute for Research in Fundamental Sciences (IPM) Tehran, Iran.

PMID: 26557071 PMCID: PMC4614320 DOI: 10.3389/fncom.2015.00130

Abstract

How networks endure damage is a central issue in neural network research. In this paper, we study the slow and fast dynamics of network damage and compare the results for two simple but very different models of recurrent and feed forward neural network. What we find is that a slower degree of network damage leads to a better chance of recovery in both types of network architecture. This is in accord with many experimental findings on the damage inflicted by strokes and by slowly growing tumors. Here, based on simulation results, we explain the seemingly paradoxical observation that disability caused by lesions, affecting large portions of tissue, may be less severe than the disability caused by smaller lesions, depending on the speed of lesion growth.

Keywords: brain damage; brain network models; fast dynamics; network damage; network dynamics; network recovery; slow dynamics; spatiotemporal dynamics

References

  1. Stroke Res Treat. 2010 Dec 20;2011:879817 - PubMed
  2. Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Jan;67(1 Pt 2):015101 - PubMed
  3. J Comp Physiol Psychol. 1958 Oct;51(5):546-8 - PubMed
  4. Neuropsychologia. 1977;15(1):179-82 - PubMed
  5. J Comp Physiol Psychol. 1951 Oct;44(5):479-86 - PubMed
  6. Brain. 1996 Dec;119 ( Pt 6):1849-57 - PubMed
  7. J Neurol Neurosurg Psychiatry. 2002 Apr;72(4):511-6 - PubMed
  8. J Comp Physiol Psychol. 1972 Jun;79(3):481-7 - PubMed
  9. Front Comput Neurosci. 2009 Aug 04;3:10 - PubMed
  10. Exp Neurol. 1975 Jun;47(3):470-80 - PubMed
  11. Stroke. 1996 Jun;27(6):1105-9; discussion 1109-11 - PubMed
  12. J Neurosurg. 2003 Apr;98(4):764-78 - PubMed
  13. Hippocampus. 2008;18(9):879-98 - PubMed
  14. Phys Rev Lett. 2000 Dec 18;85(25):5468-71 - PubMed
  15. Neuron. 2009 Feb 26;61(4):621-34 - PubMed
  16. Brain. 2007 Apr;130(Pt 4):898-914 - PubMed
  17. J Neurosci. 1988 May;8(5):1704-11 - PubMed
  18. Phys Rev Lett. 2001 Apr 16;86(16):3682-5 - PubMed
  19. PLoS Comput Biol. 2009 Jun;5(6):e1000408 - PubMed
  20. Science. 1971 Jul 23;173(3994):353-6 - PubMed
  21. J Comp Physiol Psychol. 1971 Nov;77(2):221-7 - PubMed
  22. Nature. 2000 Jul 27;406(6794):378-82 - PubMed
  23. J Neurosurg. 1959 Jan;16(1):85-97; discussion 97-8 - PubMed
  24. Stroke. 2003 Jun;34(6):1553-66 - PubMed
  25. IEEE Trans Med Imaging. 2011 May;30(5):1154-65 - PubMed
  26. Trends Neurosci. 1989 Jul;12(7):265-70 - PubMed

Publication Types